Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better scheduling would rely on accurate estimation of surgery duration. Our model can achieve a good prediction result on surgery duration with a dozen of features. We have found the result of our best performing department-specific XGBoost model with the values 31.6 min, 18.71 min, 0.71, 28% and 27% for the metrics of root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination ($R^2$), mean absolute percentage error (MAPE) and proportion of estimated result within 10% variation, respectively. We have presented each department-specific result with our estimated results between 5 and 10 min deviation would be more informative to the users in the real application. Our study shows comparable performance with previous studies, and the machine learning methods use fewer features that are better suited for universal usability.

Various levels of government across Taiwan are eager to promote the establishment of long-term care residential facilities to meet the significant caregiving needs arising from the wave of population aging. However, the successful establishment of an effective mechanism relies on proper supervision and guidance. Therefore, implementing a value assessment system for long-term care service quality management is of paramount importance. Using multi-criteria decision-making (MCDM) approach can provide effective conditions for the establishment of such system and enable a more comprehensive and objective evaluation of long-term care service quality. Using this system, decision-makers can incorporate different indicators based on various needs and weights to evaluate the quality and performance of long-term care services. This facilitates the determination of priorities and the formulation of improvement strategies, thereby enhancing the quality of long-term care services. This study develops an information-based assessment model for a platform that is win-win for both institutions and individuals. The model incorporates consumer reputation and environmental social governance (ESG) dimensions, in addition to indicators such as operational and management efficiency, professional care quality, safety and environmental facilities, and protection of individual rights and interests. Further, it integrates multiple indicator items and employs the analytic hierarchy process (AHP) to decompose and structure complex multi-dimensional issues, thereby aligning itself with current corporate evaluations. It aims to assist care service agencies in making key service quality decisions across different dimensions, and enhance the overall quality and competitiveness of those agencies, while increasing public trust and recognition in the evaluation of care service quality.